Graph selection with GGMselect. Applications on inference of biological networks have raised a strong interest in the problem of graph estimation in high-dimensional Gaussian graphical models. To handle this problem, we propose a two-stage procedure which first builds a family of candidate graphs from the data, and then selects one graph among this family according to a dedicated criterion. This estimation procedure is shown to be consistent in a high-dimensional setting, and its risk is controlled by a non-asymptotic oracle-like inequality. The procedure is tested on a real data set concerning gene expression data, and its performances are assessed on the basis of a large numerical study. par The procedure is implemented in the R-package GGMselect available on the CRAN.
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References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
- Devijver, Emilie; Gallopin, Mélina: Block-diagonal covariance selection for high-dimensional Gaussian graphical models (2018)
- Bar-Hen, Avner; Poggi, Jean-Michel: Influence measures and stability for graphical models (2016)
- Charbonnier, Camille; Verzelen, Nicolas; Villers, Fanny: A global homogeneity test for high-dimensional linear regression (2015)
- Dalalyan, A. S.; Tsybakov, A. B.: Sparse regression learning by aggregation and Langevin Monte-Carlo (2012)
- Giraud, Christophe; Huet, Sylvie; Verzelen, Nicolas: High-dimensional regression with unknown variance (2012)
- Giraud, Christophe; Huet, Sylvie; Verzelen, Nicolas: Graph selection with GGMselect (2012)
- Giraud, Christophe; Tsybakov, Alexandre: Discussion: Latent variable graphical model selection via convex optimization (2012)